Research on Fracture Prediction Method Based on Multi-Source Information Fusion

Machine learning is a good method for predicting fracture by integrating multi-source information. Post-stack seismic attributes are commonly used to predict medium to large fractures, while pre-stack seismic attributes are proven to be more sensitive to small and micro sized fractures through forward modeling. Using machine learning algorithm to fuse information from different scales to predict fracture can greatly improve the accuracy of fracture prediction. On the basis of In-Situ stress prediction, the paper conducted post-stack seismic attribute analysis and pre-stack seismic attribute analysis, further studied on the sensitivity of seismic attributes to fracture and selected sensitive attributes, used the sensitivity log of well-bore fractures as the target log for learning, ultimately obtained a comprehensive body of fracture. Through blind well verification, the prediction results match well with the we1l data and the prediction results is highly consistent with the production data. The results of fracture prediction are reliable, and the research method has certain reference significance for fracture prediction.

The application study on the multi-scales integrated prediction method to fractured reservoir description

In this paper,we implement three scales of fracture integrated prediction study by classifying it to macro-（ 1/4/λ）,meso-（ 1/100λ and 1/4λ） and micro-（ 1/100λ） scales.Based on the multi-scales rock physics modelling technique,the seismic azimuthal anisotropy characteristic is analyzed for distinguishing the fractures of meso-scale.Furthermore,by integrating geological core fracture description,image well-logging fracture interpretation,seismic attributes macro-scale fracture prediction and core slice micro-scale fracture characterization,an comprehensive multi-scale fracture prediction methodology and technique workflow are proposed by using geology,well-logging and seismic multi-attributes.Firstly,utilizing the geology core slice observation（Fractures description） and image well-logging data interpretation results,the main governing factors of fracture development are obtained,and then the control factors of the development of regional macro-scale fractures are carried out via modelling of the tectonic stress field.For the meso-scale fracture description,the poststack geometric attributes are used to describe the macro-scale fracture as well,the prestack attenuation seismic attribute is used to predict the meso-scale fracture.Finally,by combining lithological statistic inversion with superposed results of faults,the relationship of the meso-scale fractures,lithology and faults can be reasonably interpreted and the cause of meso-scale fractures can be verified.The micro-scale fracture description is mainly implemented by using the electron microscope scanning of cores.Therefore,the development of fractures in reservoirs is assessed by valuating three classes of fracture prediction results.An integrated fracture prediction application to a real field in Sichuan basin,where limestone reservoir fractures developed,is implemented.The application results in the study area indicates that the proposed multi-scales integrated fracture prediction method and the technique procedureare able to deal with the strong heterogeneity and multi-scales problems in fracture prediction.Moreover,the multi-scale fracture prediction technique integrated with geology,well-logging and seismic multi-information can help improve the reservoir characterization and sweet-spots prediction for the fractured hydrocarbon reservoirs.

Seismic identification and characterization of complex storage space oil and gas reservoirs

To predict complex reservoir spaces(with developed caves,pores,and fractures),based on the results of full-azimuth depth migration processing,we adopted reverse weighted nonlinear inversion to improve the accuracy of porous reservoir prediction.Scattering imaging three-parameter wavelet transform technology was used to accurately predict small-scale cave bodies.The joint inversion method of velocity and amplitude anisotropy was developed to improve the accuracy of small and medium-sized fracture prediction.The results of multiscale fracture modeling and characterization,interwell connectivity analysis,and connection path prediction are consistent with the production condition.Finally,based on the above prediction findings,favorable reservoir development areas were predicted.The above ideas and strategies have great application value for the efficient exploration and development of complex storage space reservoirs and the optimization of high-yield well locations.

Fracture gradient prediction:an overview and an improved method

The fracture gradient is a critical parameter for drilling mud weight design in the energy industry. A new method in fracture gradient prediction is proposed based on analyzing worldwide leak-off test(LOT) data in offshore drilling. Current fracture gradient prediction methods are also reviewed and compared to the proposed method. We analyze more than 200 LOT data in several offshore petroleum basins and find that the fracture gradient depends not only on the overburden stress and pore pressure, but also on the depth. The data indicate that the effective stress coefficient is higher at a shallower depth than that at a deeper depth in the shale formations. Based on this finding,a depth-dependent effective stress coefficient is proposed and applied for fracture gradient prediction. In some petroleum basins, many wells need to be drilled through long sections of salt formations to reach hydrocarbon reservoirs.The fracture gradient in salt formations is very different from that in other sedimentary rocks. Leak-off test data in the salt formations are investigated, and a fracture gradient prediction method is proposed. Case applications are examined to compare different fracture gradient methods and validate the proposed methods. The reasons why the LOT value is higher than its overburden gradient are also explained.

Quantitative Prediction of Fracture Distribution of the Longmaxi Formation in the Dingshan Area, China using FEM Numerical Simulation

Fracture prediction is a technical issue in the field of petroleum exploration and production worldwide.Although there are many approaches to predict the distribution of cracks underground,these approaches have some limitations.To resolve these issues,we ascertained the relation between numerical simulations of tectonic stress and the predicted distribution of fractures from the perspective of geologic genesis,based on the characteristics of the shale reservoir in the Longmaxi Formation in Dingshan;the features of fracture development in this reservoir were considered.3 D finite element method(FEM)was applied in combination with rock mechanical parameters derived from the acoustic emissions.The paleotectonic stress field of the crack formation period was simulated for the Longmaxi Formation in the Dingshan area.The splitting factor in the study area was calculated based on the rock breaking criterion.The coefficient of fracture development was selected as the quantitative prediction classification criteria for the cracks.The results show that a higher coefficient of fracture development indicates a greater degree of fracture development.On the basis of the fracture development coefficient classification,a favorable area was identified for the development of fracture prediction in the study area.The prediction results indicate that the south of the Dingshan area and the DY3 well of the central region are favorable zones for fracture development.

Fracture prediction in non-isothermal viscous pressure bulging of aluminum alloy sheet using ductile fracture criterion

The failure of AA3003 aluminum alloy sheet metal was predicted for non-isothermal viscous pressure bulging (VPB). Utilizing the coupled thermo-mechanical finite element method combined with ductile fracture criterion, the calculations were carried out for non-isotherm VPB at various temperatures and the influences of the initial temperature of viscous medium on failure mode of bulge specimens were investigated. The results show that the failure modes are different for the non-isothermal VPB with different initial temperatures of viscous medium. For the non-isothermal VPB of AA3003 aluminum alloy sheet with initial temperature of 250 ℃, when the initial temperature of viscous medium ranges from 150 to 180 ℃, the formability of sheet metal can be improved to a full extent. The validity of the predictions is examined by comparing with experimental results.

3-D FRACTURE PROPAGATION SIMULATION AND PRODUCTION PREDICTION IN COALBED

In accordance with the fracturing and producing mechanism in coalbed methane well, and combining the knowledge of fluid mechanics, linear elastic fracture mechanics, thermal transfer, computing mathematics and software engineering, the three-dimensional hydraulic fracture propagating and dynamical production predicting models for coalbed methane well is put forward. The fracture propagation model takes the variation of rock mechanical properties and in-situ stress distribution into consideration. The dynamic performance prediction model takes the gas production mechanism into consideration. With these models, a three-dimensional hydraulic fracturing optimum design software for coalbed methane well is developed, and its practicality and reliability have been proved by ex-ample computation.

An intelligent prediction method of fractures in tight carbonate reservoirs

An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.

Application of Pore Evolution and Fracture Development Coupled Models in the Prediction of Reservoir "Sweet Spots" in Tight Sandstones

The Chang-63 reservoir in the Huaqing area has widely developed tight sandstone ＂thick sand layers, but not reservoirs characterized by rich in oil＂, and it is thus necessary to further study its oil and gas enrichment law. This study builds porosity and fracture development and evolution models in different deposition environments, through core observation, casting thin section, SEM, porosity and permeability analysis, burial history analysis, and ＂four-property-relationships＂ analysis.

Thermal stress and fracture temperature prediction for flexible pavement

Analytical solutions of thermal stresses in multilayered elastic system whose materials characteristics are dependent on temperature are derived by a transfer matrix and integral transformation method.The resulting formulation is used to calculate thermal stresses in the low temperature cracking problem of asphalt pavement.Numerical simulations and analyses are performed using different structural combinations and material characteristics of base course.And fracture temperatures are predicted for a given flexible pavement constructed with three types of asphalt mixtures based on the calculated results and experimental data.This approach serves as a better model for real pavement structure as it takes into account the relationships between the material characteristics and temperature in the pavement system.

Forward modeling of fracture prediction based on seismic attribute modeling

Fractured reservoirs have always been a big favorable area for oil and gas reservoirs,so prediction of fractures is also a research hotspot in recent years.Due to the diversity of fracture development and the unclear development mechanism,fracture prediction has always been a major problem.Simple numerical simulation In this paper,seismic attribute is combined with numerical simulation,logging data and actual seismic profile are used as constraints,inversion impedance value and coherent attribute are combined,and finally a property model more in line with the actual geological conditions is established.The wave equation calculation and migration processing were used to obtain the numerical simulation profile,and the actual seismic profile,fracture detection profile and numerical simulation profile were combined for analysis:①The numerical simulation section under this modeling method can greatly correspond to the actual seismic section,and the reflected results can better reflect the changes of response characteristics.②The reliability and applicability of the fracture detection technology can be determined by comparing the forward simulation profile with the fracture detection profile.

Development and validation of a predictive model for spinal fracture risk in osteoporosis patients

BACKGROUND Spinal osteoporosis is a prevalent health condition characterized by the thinning of bone tissues in the spine,increasing the risk of fractures.Given its high incidence,especially among older populations,it is critical to have accurate and effective predictive models for fracture risk.Traditionally,clinicians have relied on a combination of factors such as demographics,clinical attributes,and radiological characteristics to predict fracture risk in these patients.However,these models often lack precision and fail to include all potential risk factors.There is a need for a more comprehensive,statistically robust prediction model that can better identify high-risk individuals for early intervention.AIM To construct and validate a model for forecasting fracture risk in patients with spinal osteoporosis.METHODS The medical records of 80 patients with spinal osteoporosis who were diagnosed and treated between 2019 and 2022 were retrospectively examined.The patients were selected according to strict criteria and categorized into two groups:Those with fractures(n=40)and those without fractures(n=40).Demographics,clinical attributes,biochemical indicators,bone mineral density(BMD),and radiological characteristics were collected and compared.A logistic regression analysis was employed to create an osteoporotic fracture risk-prediction model.The area under the receiver operating characteristic curve(AUROC)was used to evaluate the model’s performance.RESULTS Factors significantly associated with fracture risk included age,sex,body mass index(BMI),smoking history,BMD,vertebral trabecular alterations,and prior vertebral fractures.The final risk-prediction model was developed using the formula:(logit[P]=-3.75+0.04×age-1.15×sex+0.02×BMI+0.83×smoking history+2.25×BMD-1.12×vertebral trabecular alterations+1.83×previous vertebral fractures).The AUROC of the model was 0.93(95%CI:0.88-0.96,P<0.001),indicating strong discriminatory capabilities.CONCLUSION The fracture risk-prediction model,utilizing accessible clinical,biochemical,and radiological information,offered a precise tool for the evaluation of fracture risk in patients with spinal osteoporosis.The model has potential in the identification of high-risk individuals for early intervention and the guidance of appropriate preventive actions to reduce the impact of osteoporosis-related fractures.

Application of Comprehensive Geophysical Techniques to Predict Carbonate Fractured Reservoirs inQingxi Oilfield

Inter-crystalline pores, cavities, and fractures created from diagenetic shrinkage of dolomite are inter-connected each other, forming fine oil- and gas-bearing reservoirs. It is hard to predict these complex fracture-cavity reservoirs because of their random distribution, different growth timing, and so on. Taking the lacustrine dolomite fracture-pore reservoir in the Lower Cretaceous Xiagou Formation in the Qingxi oilfield within the Jiuquan basin as an example, we put forward a comprehensive geophysical method to predict carbonate fractures.

Study of the relationship between fractures and highly productive shale gas zones, Longmaxi Formation, Jiaoshiba area in eastern Sichuan

Shale fractures are an important factor controlling shale gas enrichment and high-productivity zones in the Longmaxi Formation, Jiaoshiba area in eastern Sichuan. Drilling results have, however, shown that the shale fracture density does not have a straightforward correlation with shale gas productivity. Based on logging data, drilling and seismic data, the relationship between shale fracture and shale gas accumulation is investigated by integrating the results of experiments and geophysical methods. The following conclusions have been drawn：（1） Tracer diffusion tests indicate that zones of fracture act as favorable channels for shale gas migration and high-angle fractures promote gas accumulation.（2） Based on the result of azimuthal anisotropy prediction, a fracture system with anisotropy strength values between 1 and 1.15 represents a moderate development of high-angle fractures, which is considered to be favorable for shale gas accumulation and high productivity, while fracture systems with anisotropy strength values larger than 1.15 indicate over-development of shale fracture, which may result in the destruction of the shale reservoir preservation conditions.

A numerical investigation of hydraulic fracturing on coal seam permeability based on PFC‑COMSOL coupling method

Hydraulic fracturing and permeability enhancement are effective methods to improve low-permeability coal seams.However,few studies focused on methods to increase permeability,and there are no suitable prediction methods for engineering applications.In this work,PFC2D software was used to simulate coal seam hydraulic fracturing.The results were used in a coupled mathematical model of the interaction between coal seam deformation and gas flow.The results show that the displacement and velocity of particles increase in the direction of minimum principal stress,and the cracks propagate in the direction of maximum principal stress.The gas pressure drop rate and permeability increase rate of the fracture model are higher than that of the non-fracture model.Both parameters decrease rapidly with an increase in the drainage time and approach 0.The longer the hydraulic fracturing time,the more complex the fracture network is,and the faster the gas pressure drops.However,the impact of fracturing on the gas drainage effect declines over time.As the fracturing time increases,the difference between the horizontal and vertical permeability increases.However,this difference decreases as the gas drainage time increases.The higher the initial void pressure,the faster the gas pressure drops,and the greater the permeability increase is.However,the influence of the initial void pressure on the permeability declines over time.The research results provide guidance for predicting the anti-reflection effect of hydraulic fracturing in underground coal mines.

Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factors

The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained.The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.

Prediction of natural fracture in shale oil reservoir based on R/S analysis and conventional logs

Investigation into natural fractures is extremely important for the exploration and development of low-permeability reservoirs.Previous studies have proven that abundant oil resources are present in the Upper Triassic Yanchang Formation Chang 7 oil-bearing layer of the Ordos Basin,which are accumulated in typical low-permeability shale reservoirs.Natural fractures are important storage spaces and flow pathways for shale oil.In this study,characteristics of natural fractures in the Chang 7 oil-bearing layer are first analyzed.The results indicate that most fractures are shear fractures in the Heshui region,which are characterized by high-angle,unfilled,and ENE-WSW-trending strike.Subsequently,natural fracture distributions in the Yanchang Formation Chang 7 oil-bearing layer of the study area are predicted based on the R/S analysis approach.Logs of AC,CAL,ILD,LL8,and DEN are selected and used for fracture prediction in this study,and the R(n)/S(n)curves of each log are calculated.The quadratic derivatives are calculated to identify the concave points in the R(n)/S(n)curve,indicating the location where natural fracture develops.Considering the difference in sensitivity of each log to natural fracture,gray prediction analysis is used to construct a new parameter,fracture prediction indicator K,to quantitatively predict fracture development.In addition,fracture development among different wells is compared.The results show that parameter K responds well to fracture development.Some minor errors may probably be caused by the heterogeneity of the reservoir,limitation of core range and fracture size,dip angle,filling minerals,etc.

Characterization of Organic-Rich Shales for Petroleum Exploration & Exploitation: A Review-Part 3: Applied Geomechanics, Petrophysics and Reservoir Modeling

Modeling geomechanical properties of shales to make sense of their complex properties is at the forefront of petroleum exploration and exploitation application and has received much re- search attention in recent years. A shale＇s key geomechanical properties help to identify its ＂fracibility＂ its fluid flow patterns and rates, and its in-place petroleum resources and potential commercial re- serves. The models and the information they provide, in turn, enable engineers to design drilling pat- terns, fracture-stimulation programs and materials selection that will avoid formation damage and op- timize recovery of petroleum. A wide-range of tools, technologies, experiments and mathematical techniques are deployed to achieve this. Characterizing the interconnected fracture, permeability and porosity network is an essential step in understanding a shales highly-anisotropic features on multiple scales （nano to macro）. Weli-log data, and its petrophysical interpretation to calibrate many geome- chanical metrics to those measured in rock samples by laboratory techniques plays a key role in pro- viding affordable tools that can be deployed cost-effectively in multiple well bores. Likewise, micro- seismic data helps to match fracture density and propagation observed on a reservoir scale with pre- dictions from simulations and laboratory tests conducted on idealised/simplified discrete fracture net- work models. Shales complex wettability, adsorption and water imbibition characteristics have a sig- nificant influence on potential formation damage during stimulation and the short-term and long-term flow of petroleum achievable. Many gas flow mechanisms and models are proposed taking into ac- count the multiple flow mechanisms involved （e.g., desorption, diffusion, slippage and viscous flow op- erating at multiple porosity levels from nano- to macro-scales）. Fitting historical production data and well decline curves to model predictions helps to verify whether model＇s geomechanical assumptions are realistic or not. This review discusses the techniques applied and the models developed that are relevant to applied geomechanics, highlighting examples of their application and the numerous out- standin~ questions associated with them.